× Business
TelecomHealthcareDigital MarketingERPRetailMedia and EntertainmentOil and GasFood and BeveragesMarketing and AdvertisingBanking and Insurance
Technology
Big DataCloudIT ServiceSoftwareMobileSecurityNetworkingStorageCyber SecuritySAPData AnalysisloTBio Tech
Platform
Cisco DATABASE Google IBM Juniper Microsoft M2M Oracle Red hat Saas SYMANTEC
Leadership
CEO ReviewCMO ReviewCFO ReviewCompany Review
Magazines
US INDIA ASIA ARCHIVE
Startups Opinion Yearbook Readers Speak Contact Us

Process those images in a faster pace with the smart CT system

siliconreview Process those images in a faster pace with the smart CT system

Artificial intelligence and machine learning has been transforming the healthcare industries for a while. It has attained a newer level with the NVIDIA –GE partnership. When we analyze, average hospital generates some 50 petabytes of data annually but only a very small percentage of it is able to be analyzed fully or acted upon. That is one reason we need machine language and AI technologies, to assist in producing better healthcare technologies.

NVIDIA has carved out a large percentage of the AI accelerator market with its powerful Tesla GPUs in a variety of applications. Apart from this, the company is helping GE to bring out a new technology in CT system. The new CT system in the Revolution Family is two times faster in imaging processing than its predecessor, due to its use of NVIDIA’s AI computing platform. The Revolution Frontier is FDA cleared and expected to deliver better clinical outcomes in liver lesion detection and kidney lesion characterization because of its speed – potentially reducing the need for unnecessary follow-ups, benefitting patients with compromised renal function and reducing non-interpretable scans with Gemstone Spectral Imaging Metal Artefact Reduction (GSI MAR). Modules of the new analytics platform will use NVIDIA GPUs, the NVIDIA CUDA parallel computing platform and the NVIDIA GPU Cloud container registry to accelerate the creation, deployment and consumption of deep learning algorithms in new healthcare analytic applications that will be seamlessly integrated into clinical and operational workflows and equipment.

YOU MIGHT ALSO LIKE::

ENROLL FOR UPCOMING ISSUE